{"type":"ScholarlyArticle","title":"Cause-Effect Association between Event Pairs in Event Datasets","authors":[{"name":"Debarun Bhattacharjya","slug":"debarun-bhattacharjya","given":"Debarun","family":"Bhattacharjya","affiliation":"Research AI , IBM T. J. Watson Research Center"},{"name":"Tian Gao","slug":"tian-gao","given":"Tian","family":"Gao","affiliation":"Research AI , IBM T. J. Watson Research Center"},{"name":"Nicholas Mattei","slug":"nicholas-mattei","given":"Nicholas","family":"Mattei","affiliation":"Department of Computer Science , Tulane University"},{"name":"Dharmashankar Subramanian","slug":"dharmashankar-subramanian","given":"Dharmashankar","family":"Subramanian","affiliation":"Research AI , IBM T. J. Watson Research Center"}],"abstract":"Causal discovery from observational data has been intensely studied across fields of study. In this paper, we consider datasets involving irregular occurrences of various types of events over the timeline. We propose a suite of scores and related algorithms for estimating the cause-effect association between pairs of events from such large event datasets. In particular, we introduce a general framework and the use of conditional intensity rates to characterize pairwise associations between events. Discovering such potential causal relationships is critical in several domains, including health, politics and financial analysis. We conduct an experimental investigation with synthetic data and two real-world event datasets, where we evaluate and compare our proposed scores using assessments from human raters as ground truth. For a political event dataset involving interaction between actors, we show how performance could be enhanced by enforcing additional knowledge pertaining to actor identities.","doi":"10.24963/ijcai.2020/167","url":"https://ijcai.org/proceedings/2020/167","pdf":"https://ijcai.org/proceedings/2020/0167.pdf","markdown":"https://ijcai.org/proceedings/2020/167.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/167","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":167,"pages":{"first":1202,"last":1208},"keywords":["Machine Learning: Time-series;Data Streams","Knowledge Representation and Reasoning: Action, Change and Causality","Data Mining: Mining Spatial, Temporal Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24872/file_24872.m3u8","length_sec":298,"provider":"hls"}],"references":[{"position":1,"raw":"[Acharya, 2014] S. 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